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Journal articles on the topic 'Quantitative proteomic analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Kamal, Abu Hena Mostafa, Jong-Soon Choi, Yong-Gu Cho, Hong-Sig Kim, Beom-Heon Song, Chul-Won Lee, and Sun-Hee Woo. "Comprehensive proteome analysis using quantitative proteomic technologies." Journal of Plant Biotechnology 37, no. 2 (June 30, 2010): 196–204. http://dx.doi.org/10.5010/jpb.2010.37.2.196.

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2

Yaacob, Mohamad Fakhri, Nur Anisah Johari, Alya Nur Athirah Kamaruzzaman, and Mohd Fakharul Zaman Raja Yahya. "Mass Spectrometry-Based Proteomic Investigation of Heterogeneous Biofilms: A Review." Scientific Research Journal 18, no. 2 (September 1, 2021): 67–87. http://dx.doi.org/10.24191/srj.v18i2.11718.

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Biofilm represents a major public health concern. It is a highly structured and heterogeneous microbial population that is well protected by a hydrated extracellular matrix. In most cases, the difficulties in combating a wide spectrum of biofilm-associated diseases are due to the presence of dormant cells and differential molecular expression. Proteomics is the large-scale and systematic study of cellular proteome expression at any given time by mass spectrometry. It allows high-sensitivity and high-specificity identification of differentially expressed proteins in the biofilms. Over the past few decades, multiple lines of proteomic works have successfully elucidated various aspects of the biofilm including developmental stages, antimicrobial resistance, and survival mechanisms. However, the heterogeneity of biofilms may contribute to inconsistent proteome expression throughout a proteomic experiment. This is due to the fact that the mature biofilm is often associated with the mixture between monolayer and multilayer biofilms, thick microbial population, and chemical gradient of nutrients. This review highlights the biofilm heterogeneities, the principle of mass spectrometry in proteomics, and the possible strategies for quantitative proteomic analysis of heterogeneous biofilms. It is suggested that isolation of monolayer biofilm, laser capture microdissection, flow cytometry, and subtractive proteome profiling may be considered for an accurate and reliable quantitative proteomics experiment.

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Chignell, Jeremy F., Christin Schlegel, Roland Ulber, and Kenneth F. Reardon. "Quantitative proteomic analysis ofLactobacillus delbrueckiissp.lactisbiofilms." AIChE Journal 64, no. 12 (October 31, 2018): 4341–50. http://dx.doi.org/10.1002/aic.16449.

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4

Eckersall, David. "321 ASAS-EAAP Talk: Quantitative proteomics in animal and veterinary science: a pipeline for exploiting advanced analytical technology." Journal of Animal Science 98, Supplement_4 (November 3, 2020): 55–56. http://dx.doi.org/10.1093/jas/skaa278.100.

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Abstract Application of proteomic investigation to veterinary and animal sciences has grown over the last decade, but has still not reached its full potential of application in animal health and production research [1]. Nevertheless, establishing a versatile methodology has allowed the application of quantitative proteomics for increased understanding of physiological and pathophysiological challenges, and especially to identify potential biomarkers of disease in a range of animal species. A pipeline of sample preparation and mass spectrometry followed by statistical, bioinformatic and biochemical analyses has been established to deal with biofluids and tissue samples from cattle, sheep, pigs, chickens, dogs and cats as well as wild animals. Quantitative proteomic investigation of milk in an experimental model of Streptococcus uberis mastitis of dairy cows has identified potential novel biomarkers with implications for diagnosis and treatment of this major disease. Proteins in milk which have potential as disease biomarkers, such as cathelicidin, haptoglobin and mammary associated serum amyloid A3, are significantly increased in abundance during bovine mastitis. Proteomic investigation has confirmed that these biomarkers are also increased in milk during subclinical and clinical mastitis. Proteomic analysis of plasma from chicken following stimulation of the inflammatory response to Escherichia coli lipopolysaccharide endotoxin has characterised major changes in the chicken plasma proteome. Novel biomarker candidates of hemopexin and fatty acid binding protein have been identified. This proteomic pipeline can be incorporated into many areas of research, providing novel findings at the forefront of animal and veterinary science. Such proteomic investigation requires close interdisciplinary collaboration between experts in mass spectrometry, bioinformatics, statistics and animal production in order to fully exploit recent technological advances in the omic sciences.[1] P. Bilic, et al, Proteomics in Veterinary Medicine and Animal Science: Neglected Scientific Opportunities with Immediate Impact, Proteomics. 47 (2018) 1–7. doi:10.1002/pmic.201800047.

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Tsai, Chia-Feng, Rui Zhao, Sarah M. Williams, Ronald J. Moore, Kendall Schultz, William B. Chrisler, Ljiljana Pasa-Tolic, et al. "An Improved Boosting to Amplify Signal with Isobaric Labeling (iBASIL) Strategy for Precise Quantitative Single-cell Proteomics." Molecular & Cellular Proteomics 19, no. 5 (March 3, 2020): 828–38. http://dx.doi.org/10.1074/mcp.ra119.001857.

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Mass spectrometry (MS)-based proteomics has great potential for overcoming the limitations of antibody-based immunoassays for antibody-independent, comprehensive, and quantitative proteomic analysis of single cells. Indeed, recent advances in nanoscale sample preparation have enabled effective processing of single cells. In particular, the concept of using boosting/carrier channels in isobaric labeling to increase the sensitivity in MS detection has also been increasingly used for quantitative proteomic analysis of small-sized samples including single cells. However, the full potential of such boosting/carrier approaches has not been significantly explored, nor has the resulting quantitation quality been carefully evaluated. Herein, we have further evaluated and optimized our recent boosting to amplify signal with isobaric labeling (BASIL) approach, originally developed for quantifying phosphorylation in small number of cells, for highly effective analysis of proteins in single cells. This improved BASIL (iBASIL) approach enables reliable quantitative single-cell proteomics analysis with greater proteome coverage by carefully controlling the boosting-to-sample ratio (e.g. in general <100×) and optimizing MS automatic gain control (AGC) and ion injection time settings in MS/MS analysis (e.g. 5E5 and 300 ms, respectively, which is significantly higher than that used in typical bulk analysis). By coupling with a nanodroplet-based single cell preparation (nanoPOTS) platform, iBASIL enabled identification of ∼2500 proteins and precise quantification of ∼1500 proteins in the analysis of 104 FACS-isolated single cells, with the resulting protein profiles robustly clustering the cells from three different acute myeloid leukemia cell lines. This study highlights the importance of carefully evaluating and optimizing the boosting ratios and MS data acquisition conditions for achieving robust, comprehensive proteomic analysis of single cells.

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6

Arnold, Georg J., and T. Frohlich. "Dynamic proteome signatures in gametes, embryos and their maternal environment." Reproduction, Fertility and Development 23, no. 1 (2011): 81. http://dx.doi.org/10.1071/rd10223.

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Comprehensive molecular analysis at the level of proteins represents a technically demanding, but indispensable, task since several post-transcriptional regulation mechanisms disable a valid prediction of quantitative protein expression profiles from transcriptome analysis. In crucial steps of gamete and early embryo development, de novo transcription is silenced, meaning that almost all macromolecular events take place at the level of proteins. In this review, we describe selected examples of dynamic proteome signatures addressing capacitation of spermatozoa, in vitro maturation of oocytes, effect of oestrous cycle on oviduct epithelial cells and embryo-induced alterations to the maternal environment. We also present details of the experimental strategies applied and the experiments performed to verify quantitative proteomic data. Far from being comprehensive, examples were selected to cover several mammalian species as well as the most powerful proteomic techniques currently applied. To enable non-experts in the field of proteomics to appraise published proteomic data, our examples are preceded by a customised description of quantitative proteomic methods, covering 2D difference gel electrophoresis (2D-DIGE), nano-liquid chromatography combined with tandem mass spectrometry, and label-free as well as stable-isotope labelling strategies for mass spectrometry-based quantifications.

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7

KANAZAWA, Mitsuhiro, Hisae ANYOJI, Hsiao-kun TU, Umpei NAGASHIMI, and Atsushi OGIWARA. "Quantitative Proteomic Analysis System: i-OPAL." Journal of Computer Chemistry, Japan 9, no. 4 (2010): 197–204. http://dx.doi.org/10.2477/jccj.h2117.

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8

Huang, He, Shu Lin, Benjamin A. Garcia, and Yingming Zhao. "Quantitative Proteomic Analysis of Histone Modifications." Chemical Reviews 115, no. 6 (February 17, 2015): 2376–418. http://dx.doi.org/10.1021/cr500491u.

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9

Pflieger, Delphine, Martin A. Jünger, Markus Müller, Oliver Rinner, Hookeun Lee, Peter M. Gehrig, Matthias Gstaiger, and Ruedi Aebersold. "Quantitative Proteomic Analysis of Protein Complexes." Molecular & Cellular Proteomics 7, no. 2 (October 23, 2007): 326–46. http://dx.doi.org/10.1074/mcp.m700282-mcp200.

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Pham, Trong Khoa, Pawel Sierocinski, John van der Oost, and Phillip C. Wright. "Quantitative Proteomic Analysis ofSulfolobus solfataricusMembrane Proteins." Journal of Proteome Research 9, no. 2 (February 5, 2010): 1165–72. http://dx.doi.org/10.1021/pr9007688.

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11

Tabchy, A., B. T. Hennessy, A. M. Gonzalez-Angulo, F. M. Bernstam, Y. Lu, and G. B. Mills. "Quantitative proteomic analysis in breast cancer." Drugs of Today 47, no. 2 (2011): 169. http://dx.doi.org/10.1358/dot.2011.47.2.1576695.

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Unwin, Richard D., Duncan L. Smith, David Blinco, Claire L. Wilson, Crispin J. Miller, Caroline A. Evans, Ewa Jaworska, et al. "Quantitative proteomics reveals posttranslational control as a regulatory factor in primary hematopoietic stem cells." Blood 107, no. 12 (June 15, 2006): 4687–94. http://dx.doi.org/10.1182/blood-2005-12-4995.

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Abstract The proteome is determined by rates of transcription, translation, and protein turnover. Definition of stem cell populations therefore requires a stem cell proteome signature. However, the limit to the number of primary cells available has restricted extensive proteomic analysis. We present a mass spectrometric method using an isobaric covalent modification of peptides for relative quantification (iTRAQ), which was employed to compare the proteomes of approximately 1 million long-term reconstituting hematopoietic stem cells (Lin–Sca+Kit+; LSK+) and non–long-term reconstituting progenitor cells (Lin–Sca+Kit–; LSK–), respectively. Extensive 2-dimensional liquid chromatography (LC) peptide separation prior to mass spectrometry (MS) enabled enhanced proteome coverage with relative quantification of 948 proteins. Of the 145 changes in the proteome, 54% were not seen in the transcriptome. Hypoxia-related changes in proteins controlling metabolism and oxidative protection were observed, indicating that LSK+ cells are adapted for anaerobic environments. This approach can define proteomic changes in primary samples, thereby characterizing the molecular signature of stem cells and their progeny.

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13

Funke, Sebastian, Carsten Schmelter, Sascha D. Markowitsch, Natarajan Perumal, Janis C. Heyne, Katharina Bell, Norbert Pfeiffer, and Franz H. Grus. "Comparative Quantitative Analysis of Porcine Optic Nerve Head and Retina Subproteomes." International Journal of Molecular Sciences 20, no. 17 (August 29, 2019): 4229. http://dx.doi.org/10.3390/ijms20174229.

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Optic nerve head (ONH) and retina (RET) are the main sites of damage in neurodegenerative optic neuropathies including glaucoma. Up to date, little is known about the molecular interplay between these two adjoining ocular components in terms of proteomics. To close this gap, we investigated ONH and RET protein extracts derived from porcine eyes (n = 12) (Sus scrofa domestica Linnaeus 1758) using semi-quantitative mass spectrometry (MS)-based proteomics comprising bottom-up LC–ESI MS/MS and targeted SPE-MALDI-TOF MS analysis. In summary, more than 1600 proteins could be identified from the ONH/RET tissue complex. Moreover, ONH and RET displayed tissue-specific characteristics regarding their qualitative and semi-quantitative protein compositions. Gene ontology (GO)-based functional and protein–protein interaction analyses supported a close functional connection between the metabolic-related RET and the structural-associated ONH subproteomes, which could be affected under disease conditions. Inferred from the MS findings, stress-associated proteins including clusterin, ceruloplasmin, and endoplasmin can be proposed as extracellular mediators of the ONH/ RET proteome interface. In conclusion, ONH and RET show obvious proteomic differences reflecting characteristic functional features which have to be considered for future protein biomarker profiling studies.

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14

SUI, WEIGUO, RUOHAN ZHANG, JIEJING CHEN, HUIYAN HE, ZHENZHEN CUI, MINGLIN OU, LI GUO, SHAN CONG, WEN XUE, and YONG DAI. "Comparative proteomic analysis of membranous nephropathy biopsy tissues using quantitative proteomics." Experimental and Therapeutic Medicine 9, no. 3 (January 21, 2015): 805–10. http://dx.doi.org/10.3892/etm.2015.2197.

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15

Walther, Tobias C., and Matthias Mann. "Mass spectrometry–based proteomics in cell biology." Journal of Cell Biology 190, no. 4 (August 23, 2010): 491–500. http://dx.doi.org/10.1083/jcb.201004052.

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The global analysis of protein composition, modifications, and dynamics are important goals in cell biology. Mass spectrometry (MS)–based proteomics has matured into an attractive technology for this purpose. Particularly, high resolution MS methods have been extremely successful for quantitative analysis of cellular and organellar proteomes. Rapid advances in all areas of the proteomic workflow, including sample preparation, MS, and computational analysis, should make the technology more easily available to a broad community and turn it into a staple methodology for cell biologists.

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16

Li, Na, Huanni Li, Lanqin Cao, and Xianquan Zhan. "Quantitative analysis of the mitochondrial proteome in human ovarian carcinomas." Endocrine-Related Cancer 25, no. 10 (October 2018): 909–31. http://dx.doi.org/10.1530/erc-18-0243.

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Mitochondria play important roles in growth, signal transduction, division, tumorigenesis and energy metabolism in epithelial ovarian carcinomas (EOCs) without an effective biomarker. To investigate the proteomic profile of EOC mitochondrial proteins, a 6-plex isobaric tag for relative and absolute quantification (iTRAQ) proteomics was used to identify mitochondrial expressed proteins (mtEPs) in EOCs relative to controls, followed by an integrative analysis of the identified mtEPs and the Cancer Genome Atlas (TCGA) data from 419 patients. A total of 5115 quantified proteins were identified from purified mitochondrial samples, and 262 proteins were significantly related to overall survival in EOC patients. Furthermore, 63 proteins were identified as potential biomarkers for the development of an EOC, and our findings were consistent with previous reports on a certain extent. Pathway network analysis identified 70 signaling pathways. Interestingly, the results demonstrated that cancer cells exhibited an increased dependence on mitophagy, such as peroxisome, phagosome, lysosome, valine, leucine and isoleucine degradation and fatty acid degradation pathways, which might play an important role in EOC invasion and metastasis. Five proteins (GLDC, PCK2, IDH2, CPT2 and HMGCS2) located in the mitochondrion and enriched pathways were selected for further analysis in an EOC cell line and tissues, and the results confirmed reliability of iTRAQ proteomics. These findings provide a large-scale mitochondrial proteomic profiling with quantitative information, a certain number of potential protein biomarkers and a novel vision in the mitophagy bio-mechanism of a human ovarian carcinoma.

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17

Röst, Hannes L., Lars Malmström, and Ruedi Aebersold. "Reproducible quantitative proteotype data matrices for systems biology." Molecular Biology of the Cell 26, no. 22 (November 5, 2015): 3926–31. http://dx.doi.org/10.1091/mbc.e15-07-0507.

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Historically, many mass spectrometry–based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.

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18

Wang, Zongkui, Rong Zhang, Fengjuan Liu, Peng Jiang, Jun Xu, Haijun Cao, Xi Du, et al. "TMT‐Based Quantitative Proteomic Analysis Reveals Proteomic Changes Involved in Longevity." PROTEOMICS – Clinical Applications 13, no. 4 (November 28, 2018): 1800024. http://dx.doi.org/10.1002/prca.201800024.

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19

Bradshaw, Amy D., Catalin F. Baicu, John H. Schwacke, Kentaro Yamane, Tyler J. Rentz, John M. Lacy, Thomas N. Gallien, Kevin L. Schey, and Michael R. Zile. "Quantitative proteomic analysis of hypertrophied rat myocardium." Matrix Biology 27 (December 2008): 30. http://dx.doi.org/10.1016/j.matbio.2008.09.300.

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20

Shiio, Yuzuru, Sam Donohoe, Eugene C. Yi, David R. Goodlett, Ruedi Aebersold, and Robert N. Eisenman. "Quantitative proteomic analysis of Myc oncoprotein function." EMBO Journal 21, no. 19 (October 1, 2002): 5088–96. http://dx.doi.org/10.1093/emboj/cdf525.

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Shiio, Yuzuru, Kwang S. Suh, Hookeun Lee, Stuart H. Yuspa, Robert N. Eisenman, and Ruedi Aebersold. "Quantitative Proteomic Analysis of Myc-induced Apoptosis." Journal of Biological Chemistry 281, no. 5 (November 29, 2005): 2750–56. http://dx.doi.org/10.1074/jbc.m509349200.

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22

Waanders, Leonie F., Karolina Chwalek, Mara Monetti, Chanchal Kumar, Eckhard Lammert, and Matthias Mann. "Quantitative proteomic analysis of single pancreatic islets." Proceedings of the National Academy of Sciences 106, no. 45 (October 21, 2009): 18902–7. http://dx.doi.org/10.1073/pnas.0908351106.

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23

Shiio, Yuzuru, Robert N. Eisenman, Eugene C. Yi, Sam Donohoe, David R. Goodlett, and Ruedi Aebersold. "Quantitative proteomic analysis of chromatin-associated factors." Journal of the American Society for Mass Spectrometry 14, no. 7 (July 2003): 696–703. http://dx.doi.org/10.1016/s1044-0305(03)00204-6.

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24

Queiroz, Rayner M. L., Sébastien Charneau, Samuel C. Mandacaru, Veit Schwämmle, Beatriz D. Lima, Peter Roepstorff, and Carlos A. O. Ricart. "Quantitative Proteomic and Phosphoproteomic Analysis ofTrypanosoma cruziAmastigogenesis." Molecular & Cellular Proteomics 13, no. 12 (September 15, 2014): 3457–72. http://dx.doi.org/10.1074/mcp.m114.040329.

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25

Völker-Albert, Moritz Carl, Miriam Caroline Pusch, Andreas Fedisch, Pierre Schilcher, Andreas Schmidt, and Axel Imhof. "A Quantitative Proteomic Analysis ofIn VitroAssembled Chromatin." Molecular & Cellular Proteomics 15, no. 3 (January 25, 2016): 945–59. http://dx.doi.org/10.1074/mcp.m115.053553.

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26

Lau, King Wai, Andrew R. Jones, Neil Swainston, Jennifer A. Siepen, and Simon J. Hubbard. "Capture and analysis of quantitative proteomic data." PROTEOMICS 7, no. 16 (August 2007): 2787–99. http://dx.doi.org/10.1002/pmic.200700127.

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27

Guevel, Laetitia, Jessie R. Lavoie, Carolina Perez-Iratxeta, Karl Rouger, Laurence Dubreil, Marie Feron, Sophie Talon, Marjorie Brand, and Lynn A. Megeney. "Quantitative Proteomic Analysis of Dystrophic Dog Muscle." Journal of Proteome Research 10, no. 5 (May 6, 2011): 2465–78. http://dx.doi.org/10.1021/pr2001385.

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28

Byrum, Stephanie, Nathan L. Avaritt, Samuel G. Mackintosh, Josie M. Munkberg, Brian D. Badgwell, Wang L. Cheung, and Alan J. Tackett. "A quantitative proteomic analysis of FFPE melanoma." Journal of Cutaneous Pathology 38, no. 11 (August 23, 2011): 933–36. http://dx.doi.org/10.1111/j.1600-0560.2011.01761.x.

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29

Silva, Wanderson M., Cassiana S. Sousa, Leticia C. Oliveira, Siomar C. Soares, Gustavo F. M. H. Souza, Guilherme C. Tavares, Cristiana P. Resende, et al. "Comparative proteomic analysis of four biotechnological strainsLactococcus lactisthrough label-free quantitative proteomics." Microbial Biotechnology 12, no. 2 (October 19, 2018): 265–74. http://dx.doi.org/10.1111/1751-7915.13305.

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Poulsen, T. B. G., J. S. Andersen, M. K. Kristiansen, S. Rasmusen, L. Arent-Nielsen, C. H. Nielsen, and A. Stensballe. "AB1254 PHENOTYPING OF MULTIPLE BIOFLUIDS FOR LIQUID BIOMARKERS FOR DIAGNOSTICS AND PERSONALIZED MEDICINE OF RHEUMATOID ARTHRITIS, SPONDYLOARTHRITIS AND OSTEOARTHRITIS." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 1918.1–1919. http://dx.doi.org/10.1136/annrheumdis-2020-eular.5949.

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Background:Inflammatory and autoimmune diseases include multifactual pathomechanisms and systemic responses. The etiology of the joint diseases rheumatoid arthritis (RA), spondyloarthritis (SpA) in relation to osteoarthritis (OA) remain incomplete and establishing the correct diagnose remains nontrivial. Advances in high-throughput molecular technologies have increased investigations into the utility of transcriptomic, proteomic and high-density protein arrays approaches as diagnostic tools and companion diagnostics for precision medicine. To increase our understanding of the molecular pathogenesis, we extracted synovial fluid from the joints from multiple patient groups and characterized the protein composition in relation to plasma. Basic blood tests include inflammatory markers and autoantibodies, however, now analysis speed and robustness allow more readily clinical insight biofluids.Objectives:We present recent Omics concepts and studies investigating inflammatory state and treatment outcome in different biofluids from plasma to synovial fluid accessing causalities leading to inflammation and pain. Additionally, the aim was to investigate in any proteomic findings in synovial fluid can be correlated to proteomic changes in patient plasma and can be used as biomarkers for treatment effect.Methods:Plasma and synovial fluid were investigated in multiple pathologies before and after treatment in patients (biologics; MTX; intraarticular gold). Deep proteome, PTM and EV profiling were accomplished using quantitative proteomics approaches using quantitative mass spectrometry-based analysis by DIA/PASEF followed by deep datamining. All biological samples were digested according to a Filter Aided Sample Preparation (FASP) protocol before analysis with tandem mass spectrometry (MS/MS). PTM profiling were evaluated by 4D CCS based feature finding.Results:Mass spectrometry based profiling allowed quantitative profiling of up to 480 proteins in matched synovial fluid and plasma. Complementary analysis by Olink proteomics, cytokine profiling and cell-free DNA. Multiple acute inflammatory proteins were more abundant in the RA synovial fluid, including proteins originating from neutrophil granulocytes, whereas SpA patients had a higher concentration of haptoglobin. Complementary analysis by Olink immunoassay identified significantly downregulated inflammation markers out of 96 tested in relation to antiinflammatory treatment.Conclusion:Discovery of biomarkers and/or inflammatory signatures through integration of multi-omic data allowed stratify patients for improved treatment and prognosis. Firstly, our data using next generation proteomics approaches alleviates many pitfalls of missing values and poor proteome coverage including unbiased PTM profiling without enrichment strategies.Disclosure of Interests:None declared

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Burch, Andrew R., Cody W. Yothers, Michelle R. Salemi, Brett S. Phinney, Pramod Pandey, and Annaliese K. Franz. "Quantitative label-free proteomics and biochemical analysis of Phaeodactylum tricornutum cultivation on dairy manure wastewater." Journal of Applied Phycology 33, no. 4 (May 27, 2021): 2105–21. http://dx.doi.org/10.1007/s10811-021-02483-3.

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AbstractMicroalgae cultivation on wastewater offers the dual benefit of lowering costs for feedstock production with simultaneous wastewater remediation. This study utilized biochemical and quantitative label-free proteomic approaches to evaluate the growth and proteomic response for diatom Phaeodactylum tricornutum cultivated on flushed dairy manure wastewater (DMW). Comparing several DMW dilutions (up to 60% DMW diluted in seawater) with a synthetic seawater medium indicates that biomass and lipid yields correlate with the starting nitrogen content of the DMW dilution. Phaeodactylum tricornutum cultivated on DMW exhibits elevated levels of polyunsaturated fatty acids (PUFAs), particularly docosapentaenoic acid (DPA, 22:5 n-3). Proteomic analysis revealed alterations in the regulations of proteins associated with protein metabolism, cellular signaling, transcription and translation, protein trafficking, and oxidative stress management pathways when comparing P. tricornutum cultivation on diluted DMW versus synthetic media, thus providing insights into how P. tricornutum reorganizes its proteome in response to a complex wastewater source. Graphical abstract

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Yin, Xuefei, Xiaohui Liu, Huali Shen, Hong Jin, and Pengyuan Yang. "Whole Proteome ofFusobacterium nucleatumand Quantitative Analysis of Proteomic Change in Cancer Environment." Acta Chimica Sinica 73, no. 4 (2015): 337. http://dx.doi.org/10.6023/a15010015.

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SUI, WEIGUO, ZHENZHEN CUI, RUOHAN ZHANG, WEN XUE, MINGLIN OU, GUIMIAN ZOU, JIEJING CHEN, and YONG DAI. "Comparative proteomic analysis of renal tissue in IgA nephropathy with iTRAQ quantitative proteomics." Biomedical Reports 2, no. 6 (July 23, 2014): 793–98. http://dx.doi.org/10.3892/br.2014.318.

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Xu, Shun, Zeng Li, Zhen Wang, Chenjun Zhai, Wenwei Liang, Chunhui Zhu, and Weimin Fan. "Proteomic Analysis Reveals Grb2 as a Key Regulator of Periodic Mechanical Stress Transduction in Chondrocytes." Cellular Physiology and Biochemistry 44, no. 4 (2017): 1509–25. http://dx.doi.org/10.1159/000485646.

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Background/Aims: Periodic mechanical stress could significantly promote chondrocyte proliferation and matrix synthesis. However, the mechanisms underlying the ability of chondrocyte detecting and responding to periodic mechanical stimuli have not been well delineated. Methods: Quantitative proteomic analysis was performed to construct the differently expressed proteome profiles of chondrocyte under pressure. Then a combination of Western blot, quantitative real-time PCR, lentiviral vector and histological methods were used to confirm the proteomic results and investigate the mechanoseing mechanism. Results: Growth factor receptor-bound protein 2 (Grb2), a component of integrin adhesome, was found a 1.49-fold increase in dynamic stress group. This process was mediated through integrin β1, leading to increased phosphorylation of focal adhesion kinase (FAK) and extracellular signal–regulated kinase 1/2 (ERK1/2) respectively and then produce the corresponding biological effects. Conclusion: This was the first time to demonstrate Grb2 has such an important role in periodic mechanotransduction, and the proteomic data could facilitate the further investigation of chondrocytes mechanosensing.

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Yu, Xiaolan, Yongsheng Wang, Markus V. Kohnen, Mingxin Piao, Min Tu, Yubang Gao, Chentao Lin, Zecheng Zuo, and Lianfeng Gu. "Large Scale Profiling of Protein Isoforms Using Label-Free Quantitative Proteomics Revealed the Regulation of Nonsense-Mediated Decay in Moso Bamboo (Phyllostachys edulis)." Cells 8, no. 7 (July 19, 2019): 744. http://dx.doi.org/10.3390/cells8070744.

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Moso bamboo is an important forest species with a variety of ecological, economic, and cultural values. However, the gene annotation information of moso bamboo is only based on the transcriptome sequencing, lacking the evidence of proteome. The lignification and fiber in moso bamboo leads to a difficulty in the extraction of protein using conventional methods, which seriously hinders research on the proteomics of moso bamboo. The purpose of this study is to establish efficient methods for extracting the total proteins from moso bamboo for following mass spectrometry-based quantitative proteome identification. Here, we have successfully established a set of efficient methods for extracting total proteins of moso bamboo followed by mass spectrometry-based label-free quantitative proteome identification, which further improved the protein annotation of moso bamboo genes. In this study, 10,376 predicted coding genes were confirmed by quantitative proteomics, accounting for 35.8% of all annotated protein-coding genes. Proteome analysis also revealed the protein-coding potential of 1015 predicted long noncoding RNA (lncRNA), accounting for 51.03% of annotated lncRNAs. Thus, mass spectrometry-based proteomics provides a reliable method for gene annotation. Especially, quantitative proteomics revealed the translation patterns of proteins in moso bamboo. In addition, the 3284 transcript isoforms from 2663 genes identified by Pacific BioSciences (PacBio) single-molecule real-time long-read isoform sequencing (Iso-Seq) was confirmed on the protein level by mass spectrometry. Furthermore, domain analysis of mass spectrometry-identified proteins encoded in the same genomic locus revealed variations in domain composition pointing towards a functional diversification of protein isoform. Finally, we found that part transcripts targeted by nonsense-mediated mRNA decay (NMD) could also be translated into proteins. In summary, proteomic analysis in this study improves the proteomics-assisted genome annotation of moso bamboo and is valuable to the large-scale research of functional genomics in moso bamboo. In summary, this study provided a theoretical basis and technical support for directional gene function analysis at the proteomics level in moso bamboo.

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DING, YIBING, MIN YANG, SHA SHE, HAIYAN MIN, XIAOMING XV, XIAOPING RAN, YONGZHENG WU, et al. "iTRAQ-based quantitative proteomic analysis of cervical cancer." International Journal of Oncology 46, no. 4 (January 29, 2015): 1748–58. http://dx.doi.org/10.3892/ijo.2015.2859.

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Reiland, Sonja, Ghasem Hosseini Salekdeh, and Jeroen Krijgsveld. "Defining pluripotent stem cells through quantitative proteomic analysis." Expert Review of Proteomics 8, no. 1 (February 2011): 29–42. http://dx.doi.org/10.1586/epr.10.100.

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Lv, Lin-Li, and Bi-Cheng Liu. "High-throughput antibody microarrays for quantitative proteomic analysis." Expert Review of Proteomics 4, no. 4 (August 2007): 505–13. http://dx.doi.org/10.1586/14789450.4.4.505.

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Rosenegger, David, Cynthia Wright, and Ken Lukowiak. "A quantitative proteomic analysis of long-term memory." Molecular Brain 3, no. 1 (2010): 9. http://dx.doi.org/10.1186/1756-6606-3-9.

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Kubota, Takashi, David A. Stead, Shin-ichiro Hiraga, Sara ten Have, and Anne D. Donaldson. "Quantitative proteomic analysis of yeast DNA replication proteins." Methods 57, no. 2 (June 2012): 196–202. http://dx.doi.org/10.1016/j.ymeth.2012.03.012.

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Lau, Ling San, Mojca Stampar, Jerome Staal, Huizhen Zhang, Stefan Pfister, Paul Northcott, Michael Taylor, et al. "MB-44SUBGROUP-SPECIFIC QUANTITATIVE PROTEOMIC ANALYSIS OF MEDULLOBLASTOMA." Neuro-Oncology 18, suppl 3 (June 2016): iii106.4—iii106. http://dx.doi.org/10.1093/neuonc/now076.42.

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Cai, Xin-Zhang, Wei-Qun Zeng, Yi Xiang, Yi Liu, Hong-Min Zhang, Hong Li, Sha She, Min Yang, Kun Xia, and Shi-Fang Peng. "iTRAQ-Based Quantitative Proteomic Analysis of Nasopharyngeal Carcinoma." Journal of Cellular Biochemistry 116, no. 7 (May 12, 2015): 1431–41. http://dx.doi.org/10.1002/jcb.25105.

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Prior, Matthew J., Mark Larance, Robert T. Lawrence, Jamie Soul, Sean Humphrey, James Burchfield, Carol Kistler, et al. "Quantitative Proteomic Analysis of the Adipocyte Plasma Membrane." Journal of Proteome Research 10, no. 11 (November 4, 2011): 4970–82. http://dx.doi.org/10.1021/pr200446r.

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Kuang, Sufang, Xin Fan, and Ren Peng. "Quantitative proteomic analysis ofRhodococcus ruberresponsive to organic solvents." Biotechnology & Biotechnological Equipment 32, no. 6 (November 2, 2018): 1418–30. http://dx.doi.org/10.1080/13102818.2018.1533432.

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Acevedo, Flor E., Bruce A. Stanley, Anne Stanley, Michelle Peiffer, Dawn S. Luthe, and Gary W. Felton. "Quantitative proteomic analysis of the fall armyworm saliva." Insect Biochemistry and Molecular Biology 86 (July 2017): 81–92. http://dx.doi.org/10.1016/j.ibmb.2017.06.001.

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Sokolowska, Izabela, Armand G. Ngounou Wetie, Alisa G. Woods, and Costel C. Darie. "Applications of Mass Spectrometry in Proteomics." Australian Journal of Chemistry 66, no. 7 (2013): 721. http://dx.doi.org/10.1071/ch13137.

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Characterisation of proteins and whole proteomes can provide a foundation to our understanding of physiological and pathological states and biological diseases or disorders. Constant development of more reliable and accurate mass spectrometry (MS) instruments and techniques has allowed for better identification and quantification of the thousands of proteins involved in basic physiological processes. Therefore, MS-based proteomics has been widely applied to the analysis of biological samples and has greatly contributed to our understanding of protein functions, interactions, and dynamics, advancing our knowledge of cellular processes as well as the physiology and pathology of the human body. This review will discuss current proteomic approaches for protein identification and characterisation, including post-translational modification (PTM) analysis and quantitative proteomics as well as investigation of protein–protein interactions (PPIs).

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Bhawal, Ruchika, Ann L. Oberg, Sheng Zhang, and Manish Kohli. "Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer." Cancers 12, no. 9 (August 27, 2020): 2428. http://dx.doi.org/10.3390/cancers12092428.

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Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

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Grande, Giuseppe, Federica Vincenzoni, Francesca Mancini, Ferran Barrachina, Antonella Giampietro, Massimo Castagnola, Andrea Urbani, Rafael Oliva, Domenico Milardi, and Alfredo Pontecorvi. "Quantitative Analysis of the Seminal Plasma Proteome in Secondary Hypogonadism." Journal of Clinical Medicine 8, no. 12 (December 3, 2019): 2128. http://dx.doi.org/10.3390/jcm8122128.

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In the grey zone of testosterone levels between 8 and 12 nmol/L, the usefulness of therapy is controversial; as such, markers of tissue action of androgens may be helpful in adjusting clinical decisions. To better understand the effect of the hypothalamic-pituitary-testicular axis on male accessory secretion, we performed a proteomic quantitative analysis of seminal plasma in patients with secondary hypogonadism, before and after testosterone replacement therapy (TRT). Ten male patients with postsurgical hypogonadotrophic hypogonadism were enrolled in this study, and five of these patients were evaluated after testosterone treatment. Ten men with proven fertility were selected as a control group. An aliquot of seminal plasma from each individual was subjected to an in-solution digestion protocol and analyzed using an Ultimate 3000 RSLC-nano HPLC apparatus coupled to a LTQ Orbitrap Elite mass spectrometer. The label-free quantitative analysis was performed via Precursor Ions Area Detector Node. Eleven proteins were identified as decreased in hypogonadic patients versus controls, which are primarily included in hydrolase activity and protein binding activity. The comparison of the proteome before and after TRT comes about within the discovery of six increased proteins. This is the primary application of quantitative proteomics pointed to uncover a cluster of proteins reflecting an impairment not only of spermatogenesis but of the epididymal and prostate epithelial cell secretory function in male hypogonadism. The identified proteins might represent putative clinical markers valuable within the follow-up of patients with distinctive grades of male hypogonadism.

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Rüetschi, Ulla, Martin Stenson, Sverker Hasselblom, Herman Nilsson-Ehle, Ulrika Hansson, Henrik Fagman, and Per-Ola Andersson. "SILAC-Based Quantitative Proteomic Analysis of Diffuse Large B-Cell Lymphoma Patients." International Journal of Proteomics 2015 (April 28, 2015): 1–12. http://dx.doi.org/10.1155/2015/841769.

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Diffuse large B-cell lymphoma (DLBCL), the most common lymphoma, is a heterogeneous disease where the outcome for patients with early relapse or refractory disease is very poor, even in the era of immunochemotherapy. In order to describe possible differences in global protein expression and network patterns, we performed a SILAC-based shotgun (LC-MS/MS) quantitative proteomic analysis in fresh-frozen tumor tissue from two groups of DLBCL patients with totally different clinical outcome: (i) early relapsed or refractory and (ii) long-term progression-free patients. We could identify over 3,500 proteins; more than 1,300 were quantified in all patients and 87 were significantly differentially expressed. By functional annotation analysis on the 66 proteins overexpressed in the progression-free patient group, we found an enrichment of proteins involved in the regulation and organization of the actin cytoskeleton. Also, five proteins from actin cytoskeleton regulation, applied in a supervised regression analysis, could discriminate the two patient groups. In conclusion, SILAC-based shotgun quantitative proteomic analysis appears to be a powerful tool to explore the proteome in DLBCL tumor tissue. Also, as progression-free patients had a higher expression of proteins involved in the actin cytoskeleton protein network, such a pattern indicates a functional role in the sustained response to immunochemotherapy.

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Liu, Xinna, Qi Wang, Zhengbo Shao, Shiqi Zhang, Mingying Hou, Menglu Jiang, Mengxian Du, Jing Li, and Huiping Yuan. "Proteomic analysis of aged and OPTN E50K retina in the development of normal tension glaucoma." Human Molecular Genetics 30, no. 11 (April 15, 2021): 1030–44. http://dx.doi.org/10.1093/hmg/ddab099.

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Abstract Progressive degeneration of retinal ganglion cells (RGCs) is a major characteristic of glaucoma, whose underlying mechanisms are still largely unknown. An E50K mutation in the Optineurin (OPTN) gene is a leading cause of normal tension glaucoma (NTG), directly affecting RGCs without high intraocular pressure and causing severe glaucomatous symptoms in clinical settings. A systematic analysis of the NTG mouse model is crucial for better understanding of the underlying pathological mechanisms for glaucoma. To elucidate proteomic and biochemical pathway alterations during NTG development, we established an OPTN E50K mutant mouse model through CRISPR/Cas9. Retinal proteins from resulting mice exhibiting glaucomatous phenotypes were subject to tandem mass tag-labeled quantitative proteomics and then analyzed through bioinformatics methods to characterize the molecular and functional signatures of NTG. We identified 6364 quantitative proteins in our proteomic analysis. Bioinformatics analysis revealed that OPTN E50K mice experienced protein synthesis dysregulation, age-dependent energy defects and autophagy-lysosome pathway dysfunction. Certain biological features, including amyloid deposition, RNA splicing, microglia activation and reduction of crystallin production, were similar to Alzheimer’s disease. Our study is the first to describe proteomic and biochemical pathway alterations in NTG pathogenesis during disease advancement. Several proteomic signatures overlapped with retinal changes found in the ad mice model, suggesting the presence of common mechanisms between age-related degenerative disorders, as well as prospective new targets for diagnostic and therapeutic strategies.

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